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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
    </image>
    <item>
      <title>Smart Trailer Technologies for Data-Driven Safety, Efficiency, and Policy Innovation in Heavy-Duty Fleet Operations</title>
      <link>https://rip.trb.org/View/2778229</link>
      <description><![CDATA[In 2022, 4,764 fatalities in the United States involved heavy-duty trucks, with 66% of these deaths affecting passenger vehicle occupants. Many resulted from underride collisions, where smaller vehicles slide under the high-clearance sides or rear of trailers, often causing catastrophic outcomes. Despite the severity of this issue, the transportation industry lacks a clear, standardized definition of “smart trailers” and guidelines for evaluating and mitigating such risks. This absence hinders the development of cohesive safety policies, the adoption of effective safety measures, and the creation of implementation roadmaps. Unlike Europe, where side underride guards are mandated and proven effective, the U.S. has yet to establish similar frameworks, contributing to uncertainty for policymakers, fleet managers, and technology developers.

This project addresses these challenges by clarifying the definition of smart trailers and establishing a data-driven framework to evaluate their safety, efficiency, and economic benefits. Smart trailers integrate advanced sensors, telematics, and artificial intelligence (AI) to monitor operations, predict risks, and optimize maintenance schedules. A key focus is determining the direct and indirect costs and benefits of equipping trailers with side and rear underride guards and evaluating proactive warning systems or additional technical and policy solutions to reduce trailer-related collisions. Tackling the limited real incident data, the study will integrate data-driven analysis with physics-informed machine learning to guide policymakers in developing practical, safety-enhancing regulations.

To achieve these objectives, the project will synthesize data and physics-based models from multiple sources, including accident reports, vehicle physics models, historical crash data, regulatory documents, and insurance industry records. This information will support the development of simulation-driven models to assess both the safety and economic implications of underride guards or alternative solutions. By leveraging these models, the research will provide actionable insights into the optimal design and implementation of safety measures and how existing smart trailer technologies can support evidence-based regulatory decisions.

Collaboration with key partners will ensure the research's practical relevance and impact. Clarience Technologies will evaluate current smart trailer technologies, identify limitations, and help define a standardized framework. Safety Emissions Solutions will contribute inspection and regulatory data to enhance cost-benefit analyses, while the Regional Industrial Development Corporation (RIDC) will assess how smart trailer technologies can address regional transportation challenges and resource allocation challenges. Pitt Ohio’s participation in validating the system through real-world testing and operational data collection is tentative and subject to confirmation.

The outcomes of this project include a standardized definition of smart trailers, recommendations for safety policy, and a framework for adopting advanced safety technologies. By addressing critical gaps in definitions, data, and evaluation methods, this research will guide the development of effective regulations and implementation roadmaps. Through collaboration with industry leaders, this project aims to enhance public safety, operational efficiency, and sustainable fleet management, paving the way for a safer and more technologically integrated future in heavy-duty transportation.]]></description>
      <pubDate>Thu, 17 Sep 2026 16:22:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2778229</guid>
    </item>
    <item>
      <title>Enhancing Transportation Policy and Planning for Safety</title>
      <link>https://rip.trb.org/View/2778221</link>
      <description><![CDATA[Transportation policy studies and improved planning are essential for furthering goals of the University Transportation Centers and the US Department of Transportation.  This project is intended to build upon long-standing and successful activities in these areas.  Three tasks are envisioned.  
First, the research team will continue to produce policy briefs and professional papers on safety and enhancing usage of battery electric vehicles (BEV).  In their 2024-2025 Safety21 project, a forthcoming policy brief and papers were developed on light duty BEV safety and policies to enhance adoption of BEV (Naiek 2024, Harper 2024, Hendrickson 2025).  Safety concerns addressed included BEV fires, vehicle weight and stopping distance.  Enhancing adoption was informed from ownership characteristics compiled from the 2022 National Household Travel Survey and existing purchase subsidies (Wang 2024).  In 2025-2026, the team proposes to extend this work to other vehicle types (notably trucks) and a focus on workforce training, particularly vehicle technicians and first responders to crashes and environmental hazards (such as floods).  BEV have particular needs for avoiding electrical shocks and short circuits.  The team expects to contribute to a series of policy briefs on BEV in the next year.
Second, the team will continue activities intended to promote adoption of effective vehicle automation.  In their 2024-2025 project, the team actively participated in National Academies of Science, Engineering and Medicine (NASEM) studies on road safety research (NASEM 2024), critical issues in transportation (NASEM 2024), a global Council of Academies of Engineering and Technological Societies (CAETS) study on the e-mobility transformation (ICAETS 2024), and an empirical study of partial automation impacts.  The weekly Safety21 Smart Transportation Dispatch (produced by project participant Karen Lightman) features safety improvements due to new technologies.  In the coming year, the team plans to actively comment on proposed federal and state rules regarding automation, respond to requests for information, and track performance of partial automation vehicles in a policy brief.  This coming year has several likely rule-making activities, potential vehicle manufacturer announcements on partial automation, examination of the TESLA driving system, and possible implementation of the NASEM 2024 study recommendations.  Monitoring and influencing these changes is a challenging task.
Third, project participants will continue to work with Regional Industrial Development Corporation (RIDC) in the planning for Pennsylvania Safety Transportation and Research Track (PennSTART), a safety, training and research facility for autonomous vehicle testing and emergency responders.  The results of both tasks 1 and 2 can help inform appropriate test scenarios for PennSTART.
PennSTART capital expenditures will be used for project match.  Carnegie Mellon researchers include Chris Hendrickson (Hamerschlag University Professor of Engineering Emeritus), Corey Harper (Assistant Professor of Civil and Environmental Engineering), and Karen Lightman (Executive Director of Safety21 UTC).  
Full references appear in a supplemental information document due to space limitations here.]]></description>
      <pubDate>Thu, 17 Sep 2026 14:00:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2778221</guid>
    </item>
    <item>
      <title>Routing Autonomous Trucks on Dedicate Lanes- Phase 2</title>
      <link>https://rip.trb.org/View/2733038</link>
      <description><![CDATA[Trucks are known to have a significant impact on congestion during traffic peak hours due to their size and slower dynamics. Human operated trucks for freight transport are faced with two constraints: those imposed by the service demand and those imposed by the human driver. For long haul operations, for example, truck drivers must meet the constraints of hours of service. For short haul they must meet family and personal constraints which often do not allow them to operate during odd hours. With automation the human constraints are removed which opens the way to view truck routing and scheduling under different and more flexible constraints. The major problem faced by automated trucks operating with the rest of traffic, however, is safety as due to the different sizes involved the sensing problem is more challenging and potential accidents can be catastrophic. Moving trucks from times of high congestion to times of no congestion will bring considerable benefits to trucking companies as well as to all other users of the road network, as fewer trucks will be operating during peak traffic hours. In addition, trucking companies that are short of truck drivers will be able to operate without disruptions and without human imposed constraints, saving on labor costs.

During the first phase of the project, the research team developed microscopic traffic simulation model which the team validated using real data from I-710. The network considered was part of I-710 and the team assumed as a first step single origin-destination (OD) flows. The team considered the scenario where trucks sharing the same road network as passenger cars become automated and operate on dedicated truck lanes at times that the traffic demand is very low, so that lanes can be switched dynamically to dedicated automated truck lanes without affecting traffic. By doing so we can keep the automated trucks separated from manually driven vehicles, thereby addressing the issue of safety.

The ongoing phase 1 study shows that by removing a number of trucks which are about 0.4% of all vehicles during a high peak traffic and have them automated and operating on dynamically dedicated lanes during off peak traffic the travel time for trucks is reduced by 4.5% while the travel time of passenger vehicles during the high peak traffic decreases by about 3%. These preliminary findings suggest that temporal rescheduling of freight demand, combined with dynamic lane management, could improve both freight and overall network performance. In phase 1 the team simply used the traffic simulator to test their ad hoc approach of moving trucks from high peak to low peak traffic without any form of optimization.

In phase 2 the team plans to extend the approach as follows: (1) The team will expand the road network to include some of the most popular truck routes covering short medium and long-haul scenarios. The issue of parking and refueling in the absence of driver will also be addressed. (2) The team will extend the results of phase 1 to multiple interacting OD pairs, allowing the framework to capture more realistic freight demand patterns and network-level coordination effects. (3) The team considers the case of truck platoons which will include fully automated truck platoons but also the more realistic case where the first truck in the platoon has a human driver. In other words, the lead truck will be driven by a human driving and following trucks will be electronically connected and fully automated. Truck platooning is an attractive concept as it has shown to have the potential of reducing aerodynamic drag and contribute to significant fuel savings. (4) The team plans to optimize their decisions of temporal rescheduling of freight demand, combined with dynamic lane management to achieve the best possible outcome. The team views the problem as assigning loads in 2 dimensions temporal and spatial in a way that reduces travel time and lowers fuel cost for both trucks and passenger vehicles.]]></description>
      <pubDate>Wed, 22 Jul 2026 17:28:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2733038</guid>
    </item>
    <item>
      <title>Examination of Light-Based Directed Vehicle to Everything Communications Systems for Bridge Strike Detection (Using ImpLi-Fi)</title>
      <link>https://rip.trb.org/View/2727317</link>
      <description><![CDATA[In this proposed project, the ImpLi-Fi team - consisting of the University of Michigan-Dearborn and SpectraLux, LLC - will deploy a reliable and directed light-based wireless infrastructure-to-vehicle communication technology to warn at-risk trucks of imminent bridge strikes. Once shown to be feasible, the same concept can also be extended to flash flood warning, wrong-way driving, etc. Unlike wireless communications using radio-frequency (RF), which are always omni-directional, ImpLi-Fi uses light, allowing transmissions to be focused so that they only target specific impacted vehicles, thereby avoiding the risk of annoying/desensitizing other parallel road users.]]></description>
      <pubDate>Fri, 10 Jul 2026 15:22:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2727317</guid>
    </item>
    <item>
      <title>Enhancing Commercial Motor Vehicle Safety and Compliance: Evaluating The Aries Pilot and Illegal Bypass Behavior in Oregon</title>
      <link>https://rip.trb.org/View/2726122</link>
      <description><![CDATA[Illegal bypass of weigh stations and roadside inspection facilities poses a measurable safety and compliance risk within Oregon’s commercial motor vehicle (CMV) system. When vehicles evade inspection, potential violations such as overweight operations, equipment deficiencies, and hours-of-service noncompliance may go undetected, increasing crash exposure and infrastructure damage risk. Oregon Department of Transportation's (ODOT’s) Commerce and Compliance Division (CCD) currently lacks a standardized, integrated methodology to quantify illegal bypass behavior or link bypass events to inspection outcomes, crash involvement, and carrier safety history.
OBJECTIVES: This research will deliver to ODOT: (1) A standardized and replicable data integration framework linking ARIES pilot data with CCD inspection, violation, crash, and carrier safety records. (2) Measurable and trackable performance indicators to support ongoing internal monitoring of illegal bypass activity and automated enforcement effectiveness. (3) Quantitative analysis of the magnitude, characteristics, and safety implications of illegal bypass behavior in Oregon. (4) Evaluation of ARIES pilot impacts on compliance rates, inspection targeting efficiency, enforcement productivity, and CMV safety outcomes. (5) Implementation guidance and best-practice recommendations to inform strategic investment decisions, future site deployments, and FMCSA Innovative Technology Deployment (ITD) funding applications.
This research will strengthen ODOT’s ability to detect and deter illegal bypass behavior, directly advancing Oregon’s transportation safety goals. By integrating ARIES data with inspection and crash records, CCD will be able to target high-risk vehicles more effectively, reduce unnecessary inspections of compliant carriers, and improve enforcement productivity. The project supports ODOT priorities related to Safety, Innovative Technologies, Process Improvement, and Stewardship of Public Resources by providing measurable evidence to guide enforcement modernization.]]></description>
      <pubDate>Wed, 08 Jul 2026 17:24:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2726122</guid>
    </item>
    <item>
      <title>Impact Study on Increasing Truck Permit Weight Limits: Infrastructure &amp; Economic Considerations</title>
      <link>https://rip.trb.org/View/2724819</link>
      <description><![CDATA[Oregon’s current weight restrictions (105,500 lbs.) for divisible loads are less than neighboring states like Idaho and Nevada, which permit up to 129,900 lbs. In response to industry requests for alignment with these states, Oregon Department of Transportation
(ODOT) needs a comprehensive impact assessment of what raising the weight limits will mean in terms of sustaining the current operational infrastructure its charged with maintaining. This study will evaluate infrastructure effects, highway safety, and maintenance costs, along with the implications for adaptation and community impacts. With neighboring states already designating heavier freight routes, increasing Oregon’s truck permit weight limits may support freight fleet electrification and promote regional integration of the shipping network while assessing the costs to maintain and manage this increased infrastructural burden that’s on ODOT’s horizon. This feasibility and impact study will assess selective extended weight designations in Oregon and survey existing programs nationwide while evaluating potential risks to structural integrity (pavement and bridges), traffic safety impacts, as well as community and environmental considerations. The findings will provide ODOT with data-driven insights to guide policy decisions. This study will also examine vehicle configurations and length factors necessary to maintain legal axle weights in Weight Tables 1 and 2 at a gross weight of 129,900 and determine if those lengths are consistent with the lengths allowed by the LCV (Longer Combination Vehicle) freeze in federal law.

(1) A comprehensive report containing recommendations to support an informed evaluation of increasing weight limits for divisible loads, including an infrastructure impact assessment detailing the effects of heavier loads on bridges, pavements, and highway safety, with a focus on high-frequency freight routes in Oregon. (2) An economic impact assessment will quantify the contributions of oversized freight to Oregon’s economy, balancing potential economic gains from increased freight capacity with the costs of infrastructure maintenance and safety considerations. (3) A strategic implementation plan will outline a phased approach to applying these findings, allowing ODOT to prioritize investments to engage communities and the public effectively.]]></description>
      <pubDate>Wed, 08 Jul 2026 13:35:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2724819</guid>
    </item>
    <item>
      <title>A Data-Driven and Region-Specific Optimization Framework for CCS and WIM Planning
</title>
      <link>https://rip.trb.org/View/2719328</link>
      <description><![CDATA[The primary objective is to enhance and operationalize a data-driven process for systematically managing Office of Transportation Data’s Continuous Count Stations and Weigh-in-Motion sites programs. This project will provide improved network coverage with more accurate representation of  statewide traffic and freight flow patterns, tailored approaches that account for distinct characteristics of Atlanta metropolitan area and the rest of Georgia, and a streamlined, ready to implement decision making process for Continuous Count Stations and Weigh-in-Motion sites planning and deployment.
]]></description>
      <pubDate>Thu, 25 Jun 2026 11:46:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2719328</guid>
    </item>
    <item>
      <title>Evaluation of Large Truck Parameter Requirements For Crash Testing</title>
      <link>https://rip.trb.org/View/2712181</link>
      <description><![CDATA[Research is needed to examine test-vehicle physical properties, vehicle pre-test preparation, and relevant evaluation criteria for Manual for Assessing Safety Hardware (MASH) Test Levels 4, 5, and 6, considering the current large-vehicle fleet and contemporary freight operations.

 Research needs include: Reviewing and updating the physical properties of single-unit trucks (Test Level 4), tractor-vans (Test Level 5), and tractor-tank vehicles (Test Level 6), including vehicle dimensions, mass, center-of-mass height, and other critical features, to better reflect the current large-vehicle fleet. The research should also review MASH documentation requirements for large test vehicles. Determining whether MASH test-vehicle pre-test preparation for large vehicles reflects critical and contemporary operating practices. For example, this may include determining whether rigidly anchored or translatable ballast freight distributions contribute to more severe impact conditions or vehicle instability following impact. Assessing MASH evaluation criteria, including rollover potential, occupant risk, and effects on adjacent traffic flow, in relation to the system’s intended purpose and primary function. For example, a barrier designed primarily to capture an impacting vehicle and prevent secondary collisions with roadside obstacles may be considered successful if it effectively contains and redirects the vehicle. However, research is needed to explore whether there is value in refining the evaluation criteria to include a “preferred” performance designation for systems that also minimize occupant risk, limit occupant compartment damage, and improve vehicle stability by reducing rollover potential for large vehicles.

 Potential research tasks include: Review existing physical characteristics of large vehicles in the current vehicle fleet; Review existing crash tests, including: identifying causes of testing failures and determining whether failures are associated with particular vehicle design features.Identify issues with existing acceptance criteria; Analyze large-vehicle crashes to determine the consequences of modifying acceptance criteria; Identify potential modifications to crash test procedures.
]]></description>
      <pubDate>Tue, 09 Jun 2026 15:16:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712181</guid>
    </item>
    <item>
      <title>Public-Private Partnerships for Truck Parking Capacity Expansion and Development</title>
      <link>https://rip.trb.org/View/2712179</link>
      <description><![CDATA[Truck drivers need safe, secure, and accessible truck parking to obtain the rest required under federal hours-of-service regulations for their own safety and the safety of other road users. States face challenges in constructing and maintaining public truck parking facilities with sufficient capacity and amenities to meet demand. In addition, the prohibition on commercialization at public interstate rest areas limits states’ ability to generate revenue from amenity services for commercial motor vehicle operators.

The rise in paid truck parking in the private sector has further affected drivers since many are not reimbursed for parking their vehicles, making parking fees an out-of-pocket expense. As a result, truck drivers increasingly seek unauthorized and potentially unsafe parking locations, such as freeway shoulders, exposing themselves and the motoring public to crash risks, as well as increased risk of cargo thefts.

Public–private partnerships (P3s) have been identified as a potential strategy for truck parking capacity expansion off the interstate system. Through collaboration between public agencies and private entities, additional safe truck parking options could increase parking capacity, reduce unauthorized parking in unsafe areas, minimize cargo theft risks, reduce supply chain disruptions, and improve safety outcomes.

The objectives of this research are to (1) develop guidelines for state departments of transportation and local governments to use P3 for truck parking and (2) identify best practices that could replicate proven models to advance collaboration and partnerships with the private sector for the purpose of truck parking capacity development and expansion.]]></description>
      <pubDate>Tue, 09 Jun 2026 15:04:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712179</guid>
    </item>
    <item>
      <title>Leading Permitting Practices that Harmonize Enforcement of Divisible Load Permits across Jurisdictions</title>
      <link>https://rip.trb.org/View/2681239</link>
      <description><![CDATA[A February 2023 workshop with industry and state representatives identified challenges related to inconsistent interpretation and enforcement of divisible load requirements. Although 23 CFR 658.5 provides a definition of “divisible load,” both industry and state agencies report variation in how the definition is applied across states and, in some cases, within the same state.

Some states provide written guidance for operators and enforcement personnel, while others offer limited or no formal documentation. These differences can create operational challenges, including route adjustments, additional travel time, increased fuel use, parking constraints, and scheduling complications. Operators may also receive citations in one jurisdiction for loads that are permitted in another.

OBJECTIVE: This scan will examine how divisible load requirements are interpreted and enforced across states, counties, metropolitan areas, municipalities, and other transportation agencies. It will document differences in practice, explore factors contributing to those differences, and incorporate input from industry partners regarding cross-jurisdictional challenges and potential solutions.

The scan team will: Identify and document current interpretation and enforcement practices; Evaluate variations in definitions and applications of divisible load requirements; Highlight successful initiatives that promote clarity and consistency; Assess strategies that support improved compliance across jurisdictions; Develop recommendations for consistent documentation of divisible load requirements; Propose a clear, standardized definition for consideration by AASHTO and the Federal Highway Administration (FHWA).

OBJECTIVE: The goal is to improve clarity, consistency, and coordination across jurisdictions while supporting effective enforcement and industry compliance.]]></description>
      <pubDate>Tue, 17 Mar 2026 14:58:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2681239</guid>
    </item>
    <item>
      <title>Routing Autonomous Trucks on Dedicated Lanes- Phase 1</title>
      <link>https://rip.trb.org/View/2676007</link>
      <description><![CDATA[Trucks are known to have a significant impact on congestion during traffic peak hours due to their size and slower dynamics. Human operated trucks for freight transport are faced with two constraints: those imposed by the service demand and those imposed by the human driver. For long haul operations, for example, truck drivers must meet the constraints of hours of service. For short haul they have to meet family and personal constraints which often do not allow them to operate during odd hours. With automation the human constraints are removed which opens the way to view truck routing and scheduling under different and more flexible constraints. The major problem faced by automated trucks operating with the rest of traffic, however, is safety as due to the different sizes involved the sensing problem is more challenging and potential accidents can be catastrophic.


Under this project the research team plans to analyze and evaluate the use of automated trucks that will operate on the surface network at times that the traffic demand is very low, so that lanes can be switched dynamically to dedicated automated truck lanes without affecting traffic. By doing so we can keep the automated trucks separated from manually driven vehicles which may be using the network, thereby addressing the issue of safety. This project will address the potential benefits of automated trucks on dedicated lanes operating at low volume traffic hours. In addition, it will extend the approach to automated truck platoons where automation will also lead to significant fuel savings (up to 20%) due to reduction in aerodynamic drag, bringing the potential to lower costs. Moving trucks from times of high congestion to times of no congestion will bring considerable benefits to trucking companies as well as to all other users of the road network, as fewer trucks will be operating during peak traffic hours. In addition, trucking companies that are short of truck drivers will be able to operate without disruptions and without human imposed constraints, saving on labor costs. The team plans to use as an example a network that includes Interstate 710 (I-710) and the Ports of Los Angeles/Long Beach, a route that generates considerable truck traffic. The team will identify the lanes that can be dynamically dedicated to automated trucks at certain hours and estimate the impact on congestion and fuel savings. The team will use real truck and traffic data to validate their traffic simulators which they will then use to run different scenarios.]]></description>
      <pubDate>Tue, 03 Mar 2026 16:31:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676007</guid>
    </item>
    <item>
      <title>Large Multimodal Models-based Undesignated Truck Parking Monitoring System at Rest Areas</title>
      <link>https://rip.trb.org/View/2669662</link>
      <description><![CDATA[Undesignated truck parking issues are prevalent in areas where truck parking facilities are scarce or overcrowded. When trucks park outside of dedicated spaces, they can obstruct emergency access routes, leading to public health and safety concerns, disrupt traffic flow, and increase the risk of theft. These problems are exacerbated in regions with a high demand for truck parking, such as District 8 in California, where nearly one-third of all parking incidents involve undesignated truck parking. Currently, the detection of undesignated parking relies heavily on manual enforcement, primarily through citations issued by patrol officers, which is costly and inefficient due to the significant resources required for patrols. Existing sensor-based truck parking detection systems also have less focus on undesignated parking due to lack of coverage. This project will develop an artificial intelligence (AI)-driven Large Multimodal Models (LMMs) based truck parking monitoring system that covers both designated truck parking and undesignated truck parking. It will build on existing work in the area of truck parking research, with a focus on incorporating new and innovative approaches.  Compared with traditional vision-based systems which can detect vehicles but lack the ability to interpret complex situations for undesignated truck parking, LMMs integrate both visual recognition and language interpretation to comprehend contextual information such as road signs, lane markers or surrounding environments. The research team will explore the integration of Set-of-Mark prompting with lightweight domain adaptation for LMMs, and the fine-tuned inference pipeline that takes advantage of site-specific labeled data to enable accurate, scalable truck parking monitoring for both designated and undesignated conditions. Based on data collected from the I-10 truck parking availability system through the research team’s recent project funded by Caltrans, the team will evaluate the proposed method’s effectiveness across multiple real-world parking lots under diverse visual conditions.  ]]></description>
      <pubDate>Sun, 15 Feb 2026 16:44:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669662</guid>
    </item>
    <item>
      <title>OpenRoad Link: A Public-Private Data Exchange for Safer, Smarter Trucking </title>
      <link>https://rip.trb.org/View/2646948</link>
      <description><![CDATA[Work zones, lane closures, and traffic incidents significantly impact roadway safety and efficiency. When lanes are blocked due to construction, crashes, or other disruptions, roadways no longer function as designed—leading unexpected congestion, increased crash risk, and reduced operational reliability. Many work zones are established to perform critical maintenance on aging infrastructure—essential to improving durability and extending the service life of roadways—but they also introduce temporary risks and delays that must be better managed.  Effects of lane blockages are particularly severe for commercial motor vehicles (CMVs), which require more time and space to slow or reroute and are subject to strict hours-of-service regulations that make delays especially costly. 

This project proposes to develop and evaluate a data exchange framework—OpenRoad Link—to integrate and share real-time lane closure, work zone, and incident data from the Oklahoma Department of Transportation (ODOT), the Oklahoma City and Tulsa Traffic Operations Centers (TOCs), and other key transportation and traffic enforcement partners. To build this framework, the project will first identify and assess the roadway data already collected and shared by these agencies, as well as the types of information currently accessible to the CMV industry through private telematics platforms. Building on national standards such as the Work Zone Data Exchange and SAE J2735 (the standard message set for vehicle-to-everything communications), the project will extend the data scope to include lane-blocking crashes, maintenance activities, and other short-term or unplanned restrictions not currently emphasized in existing feeds. Through collaboration with ODOT, city TOCs, and trucking industry partners—including a pilot with a major trucking company such as ABF—the project will demonstrate the delivery of curated, high-value information directly to in-cab devices or fleet management systems.  

Key tasks will include identifying and cataloging roadway and incident data currently collected by the Oklahoma Department of Transportation (ODOT) and the Traffic Operations Centers (TOCs) of Oklahoma City and Tulsa, as well as evaluating what information is already being shared with the commercial vehicle industry through private telematics platforms. The project will establish partnerships with ODOT, city transportation and public safety agencies, and private industry stakeholders to design and implement a unified, standards-compliant data exchange framework. Following the design phase, the team will develop and deploy the OpenRoad Link data feed, ensuring compliance with existing national standards and verifying data accuracy and reliability. A pilot deployment will be conducted in collaboration with a trucking company using a selected in-cab device to deliver actionable, real-time information directly to CMV drivers.  

Anticipated outcomes include improved safety for CMV drivers, a reduction in secondary crashes, enhanced freight reliability, and a validated proof-of-concept for scalable public-private data exchange. By producing a replicable model for collaboration between state DOTs and private-sector technology providers, the project aims to accelerate national adoption of interoperable safety data systems and promote safer, more efficient freight transportation. ]]></description>
      <pubDate>Tue, 06 Jan 2026 08:59:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646948</guid>
    </item>
    <item>
      <title>Enhancing Rural Freight Resilience in the Southeastern U.S.: Data-Driven Modeling and Decision Support for Supply Chain Efficiency.

</title>
      <link>https://rip.trb.org/View/2643108</link>
      <description><![CDATA[This research aims to address the issue of limited alternative routes in rural freight systems by modeling rural freight networks to identify critical vulnerabilities and evaluate potential recovery strategies. The study also proposes new methods for addressing truck parking shortages using models such as reservation and automated allocation for predicting demand and optimizing supply. The project leverages network science, emerging data sources, and simulation tools to develop methodologies for assessing the resilience of rural freight networks. Additionally, the study will explore the potential of connected and autonomous vehicles (CAVs) for improving operational efficiency and reducing parking demand, particularly for middle-mile delivery and short-range freight operations. This research directly addresses these issues by (1) Developing network-based modeling techniques to analyze rural freight resilience, (2) Identifying critical corridors and evaluating alternative routing strategies, and (3) Proposing innovative truck parking solutions to improve operational efficiency. This includes broader operational strategies such as parking reservations, staging areas near hubs or ports, route reservations, and quicker incident resolution for truckers.  ]]></description>
      <pubDate>Sat, 20 Dec 2025 17:04:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2643108</guid>
    </item>
    <item>
      <title>Guide on Truck Rest and Service Areas for Critical Supply Chain Delivery



</title>
      <link>https://rip.trb.org/View/2614489</link>
      <description><![CDATA[No abstract provided.]]></description>
      <pubDate>Mon, 27 Oct 2025 17:32:58 GMT</pubDate>
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